This proposal builds upon our previous work on computational neuroanatomy of aging, and investigates the use of advanced pattern analysis methods in early detection of imaging phenotypes of Alzheimer's Disease (AD). AD is a disease of rapidly increasing prevalence and clinical significance, in an aging population. Currently available diagnostic tests detect the disease at extremely late stages, perhaps more than a decade after it first begins to develop. A variety of imaging methods offers great promise for the detection of early signs of brain change, which can be indicative of future clinical progression to AD. However structural and functional imaging patterns characteristic of AD are subtle, spatially complex, evolving dynamically with age/time, and are overlaid on changes induced by normal aging. This raises the need for sophisticated methods for image analysis and pattern recognition. Towards this goal, Aim 3 integrates high-dimensional pattern analysis and recognition methods aiming to quantitatively describe imaging patterns that have diagnostic and predictive value. Since these patterns can be spatially complex and subtle, conventional image analysis methods can be challenged. We therefore build on the state of the art of machine learning and pattern recognition, focusing on the major challenge encountered in medical imaging: high dimensionality of imaging data relative to the available sample sizes. Optimal multi-parametric feature construction and dimensionality reduction methods are necessary to meet this challenge and are considered in Aim 2 along with multi-parametric classification and clustering methods. The foundation of the pattern analysis of the work in Aim 3 is also set in Aim 1, which deals with more low-level fundamental image analysis problem: registration, which is a process that places image data to a standardized coordinate system, thereby enabling statistical analysis of spatio-temporal imaging patterns across healthy and patient populations: an optimal mathematical representation of morphological data is developed via a group-wise registration process. We apply this method to structural, functional and amyloid deposition image data from two of the largest studies of normal aging (BLSA) and AD (ADNI).